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  <h1>Source code for nlp_architect.api.intent_extraction_api</h1><div class="highlight"><pre>
<span></span><span class="c1"># ******************************************************************************</span>
<span class="c1"># Copyright 2017-2018 Intel Corporation</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1"># ******************************************************************************</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">makedirs</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">sys</span>

<span class="kn">from</span> <span class="nn">nlp_architect.api.abstract_api</span> <span class="kn">import</span> <span class="n">AbstractApi</span>
<span class="kn">from</span> <span class="nn">nlp_architect.models.intent_extraction</span> <span class="kn">import</span> <span class="n">MultiTaskIntentModel</span><span class="p">,</span> <span class="n">Seq2SeqIntentModel</span>
<span class="kn">from</span> <span class="nn">nlp_architect</span> <span class="kn">import</span> <span class="n">LIBRARY_OUT</span>
<span class="kn">from</span> <span class="nn">nlp_architect.utils.generic</span> <span class="kn">import</span> <span class="n">pad_sentences</span>
<span class="kn">from</span> <span class="nn">nlp_architect.utils.io</span> <span class="kn">import</span> <span class="n">download_unlicensed_file</span>
<span class="kn">from</span> <span class="nn">nlp_architect.utils.text</span> <span class="kn">import</span> <span class="n">SpacyInstance</span><span class="p">,</span> <span class="n">bio_to_spans</span>


<div class="viewcode-block" id="IntentExtractionApi"><a class="viewcode-back" href="../../../generated_api/nlp_architect.api.html#nlp_architect.api.intent_extraction_api.IntentExtractionApi">[docs]</a><span class="k">class</span> <span class="nc">IntentExtractionApi</span><span class="p">(</span><span class="n">AbstractApi</span><span class="p">):</span>
    <span class="n">model_dir</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">LIBRARY_OUT</span> <span class="o">/</span> <span class="s2">&quot;intent-pretrained&quot;</span><span class="p">)</span>
    <span class="n">pretrained_model_info</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s2">&quot;model_info.dat&quot;</span><span class="p">)</span>
    <span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s2">&quot;model.h5&quot;</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prompt</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">word_vocab</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">tags_vocab</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">char_vocab</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">intent_vocab</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_download_pretrained_model</span><span class="p">(</span><span class="n">prompt</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">nlp</span> <span class="o">=</span> <span class="n">SpacyInstance</span><span class="p">(</span><span class="n">disable</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;tagger&quot;</span><span class="p">,</span> <span class="s2">&quot;ner&quot;</span><span class="p">,</span> <span class="s2">&quot;parser&quot;</span><span class="p">,</span> <span class="s2">&quot;vectors&quot;</span><span class="p">,</span> <span class="s2">&quot;textcat&quot;</span><span class="p">])</span>

<div class="viewcode-block" id="IntentExtractionApi.process_text"><a class="viewcode-back" href="../../../generated_api/nlp_architect.api.html#nlp_architect.api.intent_extraction_api.IntentExtractionApi.process_text">[docs]</a>    <span class="k">def</span> <span class="nf">process_text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
        <span class="n">input_text</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">text</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">())</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">nlp</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">input_text</span><span class="p">)</span></div>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_prompt</span><span class="p">():</span>
        <span class="n">response</span> <span class="o">=</span> <span class="nb">input</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">To download &#39;</span><span class="si">{}</span><span class="s2">&#39;, please enter YES: &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;intent_extraction&quot;</span><span class="p">))</span>
        <span class="n">res</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">res</span> <span class="o">==</span> <span class="s2">&quot;yes&quot;</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">res</span> <span class="o">==</span> <span class="s2">&quot;y&quot;</span><span class="p">):</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Downloading </span><span class="si">{}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;ner&quot;</span><span class="p">))</span>
            <span class="n">responded_yes</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Download declined. Response received </span><span class="si">{}</span><span class="s2"> != YES|Y. &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
            <span class="n">responded_yes</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="k">return</span> <span class="n">responded_yes</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_download_pretrained_model</span><span class="p">(</span><span class="n">prompt</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Downloads the pre-trained BIST model if non-existent.&quot;&quot;&quot;</span>
        <span class="n">model_info_exists</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">pretrained_model_info</span><span class="p">)</span>
        <span class="n">model_exists</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">pretrained_model</span><span class="p">)</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">model_exists</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">model_info_exists</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span>
                <span class="s2">&quot;The pre-trained models to be downloaded for the intent extraction dataset &quot;</span>
                <span class="s2">&quot;are licensed under Apache 2.0. By downloading, you accept the terms &quot;</span>
                <span class="s2">&quot;and conditions provided by the license&quot;</span>
            <span class="p">)</span>
            <span class="n">makedirs</span><span class="p">(</span><span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">model_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">prompt</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
                <span class="n">agreed</span> <span class="o">=</span> <span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">_prompt</span><span class="p">()</span>
                <span class="k">if</span> <span class="n">agreed</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
                    <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
            <span class="n">download_unlicensed_file</span><span class="p">(</span>
                <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net&quot;</span> <span class="s2">&quot;/models/intent/&quot;</span><span class="p">,</span>
                <span class="s2">&quot;model_info.dat&quot;</span><span class="p">,</span>
                <span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">pretrained_model_info</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="n">download_unlicensed_file</span><span class="p">(</span>
                <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net&quot;</span> <span class="s2">&quot;/models/intent/&quot;</span><span class="p">,</span>
                <span class="s2">&quot;model.h5&quot;</span><span class="p">,</span>
                <span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">pretrained_model</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Done.&quot;</span><span class="p">)</span>

<div class="viewcode-block" id="IntentExtractionApi.display_results"><a class="viewcode-back" href="../../../generated_api/nlp_architect.api.html#nlp_architect.api.intent_extraction_api.IntentExtractionApi.display_results">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">display_results</span><span class="p">(</span><span class="n">text_str</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">intent_type</span><span class="p">):</span>
        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;annotation_set&quot;</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">&quot;doc_text&quot;</span><span class="p">:</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">text_str</span><span class="p">])}</span>
        <span class="n">spans</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">available_tags</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span><span class="p">,</span> <span class="n">tag</span> <span class="ow">in</span> <span class="n">bio_to_spans</span><span class="p">(</span><span class="n">text_str</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
            <span class="n">spans</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;start&quot;</span><span class="p">:</span> <span class="n">s</span><span class="p">,</span> <span class="s2">&quot;end&quot;</span><span class="p">:</span> <span class="n">e</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="n">tag</span><span class="p">})</span>
            <span class="n">available_tags</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span>
        <span class="n">ret</span><span class="p">[</span><span class="s2">&quot;annotation_set&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">available_tags</span><span class="p">)</span>
        <span class="n">ret</span><span class="p">[</span><span class="s2">&quot;spans&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">spans</span>
        <span class="n">ret</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">intent_type</span>
        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;doc&quot;</span><span class="p">:</span> <span class="n">ret</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;high_level&quot;</span><span class="p">}</span></div>

<div class="viewcode-block" id="IntentExtractionApi.vectorize"><a class="viewcode-back" href="../../../generated_api/nlp_architect.api.html#nlp_architect.api.intent_extraction_api.IntentExtractionApi.vectorize">[docs]</a>    <span class="k">def</span> <span class="nf">vectorize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc</span><span class="p">,</span> <span class="n">vocab</span><span class="p">,</span> <span class="n">char_vocab</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">words</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">vocab</span><span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span> <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="n">vocab</span> <span class="k">else</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">doc</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
            <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">char_vocab</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">sentence_chars</span> <span class="o">=</span> <span class="p">[]</span>
            <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">doc</span><span class="p">:</span>
                <span class="n">word_chars</span> <span class="o">=</span> <span class="p">[]</span>
                <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">w</span><span class="p">:</span>
                    <span class="k">if</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">char_vocab</span><span class="p">:</span>
                        <span class="n">_cid</span> <span class="o">=</span> <span class="n">char_vocab</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="n">_cid</span> <span class="o">=</span> <span class="mi">1</span>
                    <span class="n">word_chars</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_cid</span><span class="p">)</span>
                <span class="n">sentence_chars</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">word_chars</span><span class="p">)</span>
            <span class="n">sentence_chars</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span>
                <span class="n">pad_sentences</span><span class="p">(</span><span class="n">sentence_chars</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">word_length</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
            <span class="p">)</span>
            <span class="k">return</span> <span class="p">[</span><span class="n">words</span><span class="p">,</span> <span class="n">sentence_chars</span><span class="p">]</span>
        <span class="k">return</span> <span class="n">words</span></div>

<div class="viewcode-block" id="IntentExtractionApi.inference"><a class="viewcode-back" href="../../../generated_api/nlp_architect.api.html#nlp_architect.api.intent_extraction_api.IntentExtractionApi.inference">[docs]</a>    <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc</span><span class="p">):</span>
        <span class="n">text_arr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">process_text</span><span class="p">(</span><span class="n">doc</span><span class="p">)</span>
        <span class="n">intent_type</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;mtl&quot;</span><span class="p">:</span>
            <span class="n">doc_vec</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">text_arr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_vocab</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">char_vocab</span><span class="p">)</span>
            <span class="n">intent</span><span class="p">,</span> <span class="n">tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">doc_vec</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
            <span class="n">intent</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">intent</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
            <span class="n">intent_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">intent_vocab</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">intent</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Detected intent type: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">intent_type</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">doc_vec</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">text_arr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_vocab</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
            <span class="n">tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">doc_vec</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">tags</span> <span class="o">=</span> <span class="n">tags</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
        <span class="n">tag_str</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">tags_vocab</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">tags</span><span class="p">]</span>
        <span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">text_arr</span><span class="p">,</span> <span class="n">tag_str</span><span class="p">):</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="se">\t</span><span class="si">{}</span><span class="se">\t</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">display_results</span><span class="p">(</span><span class="n">text_arr</span><span class="p">,</span> <span class="n">tag_str</span><span class="p">,</span> <span class="n">intent_type</span><span class="p">)</span></div>

<div class="viewcode-block" id="IntentExtractionApi.load_model"><a class="viewcode-back" href="../../../generated_api/nlp_architect.api.html#nlp_architect.api.intent_extraction_api.IntentExtractionApi.load_model">[docs]</a>    <span class="k">def</span> <span class="nf">load_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">pretrained_model_info</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
            <span class="n">model_info</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="n">model_info</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">word_vocab</span> <span class="o">=</span> <span class="n">model_info</span><span class="p">[</span><span class="s2">&quot;word_vocab&quot;</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">tags_vocab</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model_info</span><span class="p">[</span><span class="s2">&quot;tags_vocab&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;mtl&quot;</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">char_vocab</span> <span class="o">=</span> <span class="n">model_info</span><span class="p">[</span><span class="s2">&quot;char_vocab&quot;</span><span class="p">]</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">intent_vocab</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model_info</span><span class="p">[</span><span class="s2">&quot;intent_vocab&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
            <span class="n">model</span> <span class="o">=</span> <span class="n">MultiTaskIntentModel</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">model</span> <span class="o">=</span> <span class="n">Seq2SeqIntentModel</span><span class="p">()</span>
        <span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pretrained_model</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span></div></div>
</pre></div>

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